from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.linear_model import LinearRegression, SGDRegressor, Ridge
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression
from pandas import Series, DataFrame
import joblib
import pandas as pd
import xlrd
import numpy as np
# 获取数据（利用pd读取数据）
Eigen_Value = pd.read_csv(r"C:\Users\Lenovo\Desktop\机器学习数据\JUTTA DATA\预处理与特征工程之后的数据且筛选过的数据.csv"
                                ,usecols=[
        'KAYT_TULO_DATA',
        'KAYT_RAHATULO_DATA',
        'TULONS_VERONAL_DATA',
        'KANSPERHEL_DATA',
        'ASUMTUKI_DATA_x',
        'ASUNTOTULO',
        'LAPSIP_DATA',
        'VEROTTKANS_DATA',
        'MAKS_TS_DATA',
        'PERUSTULO',
        'LUKU_LLISA_DATA_x',
        'LLISAT_x',
        'VALT_ANS_DATA_x',
        'SVMAKSU_DATA_x',
        'KUNNVERO_DATA_x',
        'KIRKVERO_DATA_x',
        'PALKVAK_DATA_x',
        'MAKSVEROT_DATA_x',
        'LUKU_LLISA_DATA_y',
        'LLISAT_y',
        'KOTIHTUKI_DATA',
        'ykor',
        'KNRO',
        'LLISAT',
        'VALT_ANS_DATA_y',
        'SVMAKSU_DATA_y',
        'KUNNVERO_DATA_y',
        'KIRKVERO_DATA_y',
        'PALKVAK_DATA_y',
        'MAKSVEROT_DATA_y',
        'BRTULOT_y',
    ])

Eigen_Value.to_csv(r'C:\Users\Lenovo\Desktop\机器学习数据\JUTTA DATA\最终筛选Eigen_Value.csv')